New research into the “memristor brain” (via the cat brain), (another name for modeling machine learning and pattern recognition on neuronal synapse architectures), will appear in this months NL journal (Journal of Nano Letters). University of Michigan researcher Wei Lu (giving an upcoming talk titled “Si Memristive Devices Applied to Memory and Neuromorphic Circuits” at 2010 ISCAS conference) in the paper, titled “Nanoscale Memristor Device as Synapse in Neuromorphic System“, demonstrates that a memristor can “connect conventional circuits and support a process that is the basis for memory and learning in biological systems”. The research (at the U of M Lurie Nanofabrication Facility) is based on the speed at which a Cats brain can perform facial and pattern recognition:

(Wei) Lu has connected two electronic circuits with one memristor. He has demonstrated that this system is capable of a memory and learning process called “spike timing dependent plasticity.” This type of plasticity refers to the ability of connections between neurons to become stronger based on when they are stimulated in relation to each other. Spike timing dependent plasticity is thought to be the basis for memory and learning in mammalian brains. “We show that we can use voltage timing to gradually increase or decrease the electrical conductance in this memristor-based system. In our brains, similar changes in synapse conductance essentially give rise to long term memory,” Lu said. [UM Full Article Link]

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Science Advocacy through Education

Emerging technology provides a unique opportunity to introduce science within education. The development of Memristors, the fourth passive component type after resistors, capacitors and inductors, along with other Solid State memory devices, takes us one step further to creating cheap, powerful, distributed solutions for sensing and processing.